{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、 判断模块是否存在"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-image in c:\\users\\1\\anaconda3\\lib\\site-packages (0.23.2)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.9 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (1.13.1)\n",
      "Requirement already satisfied: networkx>=2.8 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (3.2.1)\n",
      "Requirement already satisfied: pillow>=9.1 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (10.3.0)\n",
      "Requirement already satisfied: imageio>=2.33 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (2.33.1)\n",
      "Requirement already satisfied: tifffile>=2022.8.12 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (2023.4.12)\n",
      "Requirement already satisfied: packaging>=21 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (23.2)\n",
      "Requirement already satisfied: lazy-loader>=0.4 in c:\\users\\1\\anaconda3\\lib\\site-packages (from scikit-image) (0.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: skimage2 in c:\\users\\1\\anaconda3\\lib\\site-packages (0.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install scikit-image\n",
    "%pip install skimage2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in a:\n",
    "    if 'numpy' in item:\n",
    "        print(item)\n",
    "\n",
    "for item in a:\n",
    "    if 'matplotlib' in item:\n",
    "        print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、matplotlib图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x230023b9940>,\n",
       " <matplotlib.lines.Line2D at 0x230023fe3c0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(0,100*np.pi)\n",
    "plt.plot(x,np.sin(8*x+np.pi/2),x,np.cos(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、绘制流程图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: graphviz in c:\\users\\1\\anaconda3\\lib\\site-packages (0.20.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid character '“' (U+201C) (3512396049.py, line 42)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[2], line 42\u001b[1;36m\u001b[0m\n\u001b[1;33m    decision60 -> pass [label=\"是\", style=dotted]; // 虚线表示这是最后一个条件分支的“是”路径\u001b[0m\n\u001b[1;37m                                                                    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid character '“' (U+201C)\n"
     ]
    }
   ],
   "source": [
    "digraph G {\n",
    "    // 设置图的属性\n",
    "    rankdir=TB; // 从上到下排列节点\n",
    "    node [shape=rectangle, style=filled, fillcolor=white, fontsize=12]; // 设置节点样式\n",
    "\n",
    "    // 定义节点\n",
    "    subgraph cluster_input {\n",
    "        label = \"输入\";\n",
    "        node [fillcolor=lightgrey];\n",
    "        input [label=\"成绩\"];\n",
    "    }\n",
    "\n",
    "    subgraph cluster_decision {\n",
    "        label = \"决策\";\n",
    "        decision90 [label=\"≥90?\"];\n",
    "        decision80 [label=\"≥80且<90?\"];\n",
    "        decision70 [label=\"≥70且<80?\"];\n",
    "        decision60 [label=\"≥60且<70?\"];\n",
    "    }\n",
    "\n",
    "    subgraph cluster_output {\n",
    "        label = \"输出\";\n",
    "        node [fillcolor=lightblue];\n",
    "        excellent [label=\"优秀\"];\n",
    "        good [label=\"良好\"];\n",
    "        pass [label=\"及格\"];\n",
    "        fail [label=\"不及格\"];\n",
    "    }\n",
    "\n",
    "    // 连接节点\n",
    "    input -> decision90;\n",
    "    decision90 -> excellent [label=\"是\"];\n",
    "    decision90 -> decision80 [label=\"否\"];\n",
    "\n",
    "    decision80 -> good [label=\"是\"];\n",
    "    decision80 -> decision70 [label=\"否\"];\n",
    "\n",
    "    decision70 -> pass [label=\"是\"];\n",
    "    decision70 -> decision60 [label=\"否\"];\n",
    "\n",
    "    decision60 -> fail [label=\"否\"];\n",
    "    decision60 -> pass [label=\"是\", style=dotted]; // 虚线表示这是最后一个条件分支的“是”路径\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\".\\\\A.png\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、 绘制图形的程序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444\n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                              00                                                              \n",
      "                                00       99    0700                                                           \n",
      "                                  009999999999900600                                                          \n",
      "                              99999908999999998071099                                                         \n",
      "                     48    9999999999999999990070099999 000                                                   \n",
      "                       000999999999999999900000009999000                     00000                            \n",
      "                       99900099999920000008888800999999999                000888850000009                     \n",
      "                      999999 999991088888888888880099999999              0088888888888880                     \n",
      "                  00099999999999990088888888888888009999999900   0      008888888888888800                    \n",
      "                 010099999999999990088888888888888809999999940  008    9088888888888888800                    \n",
      "                007000999999999999008888888888888870000004         84000004888888888888800                    \n",
      "          0000000070990009403999900008888888880001                         00088888888800                     \n",
      "         208880000209 999000000007700008888000                                 008888008                      \n",
      "          000008888881000088800730099990000                                      1000                         \n",
      "        9008888886888888880000000 9999200                                          400                        \n",
      "       00    009 7  0 99 98800999999900                                               00                      \n",
      "       0  9080080000016000  0099999900                  0           49                 00                     \n",
      "       00 90864    196 0800 009999 09                                   0010            00                    \n",
      "        00  009  88 09 000 00  0000           3008   000    99999   000     00           000006               \n",
      "          000   800020   003                 000      000 99999999  000     000                               \n",
      "              00000000009999000000            0000000000   9999999  000 500 00           00008                \n",
      "              79998000999999              99999 000000        00        010  9599999                          \n",
      "               9999999999999           9909909909         004000000         9059099099                        \n",
      "                999999999999960000     999999999000      96   42  400       9999999        000                \n",
      "                 9999999999999               0000 00   08780  0   08880                                       \n",
      "                  900002999999990000       00   06002  000038888888000                 100                    \n",
      "                 0 9999999999990999500    00   00  00  00889     9 98800            000                       \n",
      "                    9999999999999999920    000    00  02    4          04          00                         \n",
      "                     9999999999999999900    0     01  0     00   40 99 80          00                         \n",
      "                        8000999999999903              00 869   92  800000          00                         \n",
      "                       70 99999999999909              000         00               06                         \n",
      "                               009900900              9000         00000000       00 9                        \n",
      "                              80   0    00              00            0         00                            \n",
      "                                          000           700   700   00       9000                             \n",
      "                                             00000020490080    000  0000000008                                \n",
      "                                                          000009 0000                                         \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义图像显示的高度和宽度\n",
    "show_height = 50\n",
    "show_width = 110\n",
    "\n",
    "# 定义一个ASCII字符列表\n",
    "ascii_char = list(\"0123456789 \")\n",
    "char_len = len(ascii_char)\n",
    "\n",
    "# 读取图像文件（请确保文件路径正确）\n",
    "pic = plt.imread(\".\\\\abc11.jpg\")  # 替换为你的图像文件路径\n",
    "\n",
    "# 获取图像的高度、宽度和通道数\n",
    "pic_height, pic_width, _ = pic.shape\n",
    "\n",
    "# 将RGB图像转换为灰度图像\n",
    "gray = 0.2126 * pic[:, :, 0] + 0.7152 * pic[:, :, 1] + 0.0722 * pic[:, :, 2]\n",
    "\n",
    "# 根据灰度值映射到相应的ASCII字符\n",
    "for i in range(show_height):\n",
    "    y = int(i * pic_height / show_height)\n",
    "    text = \"\"\n",
    "    for j in range(show_width):\n",
    "        x = int(j * pic_width / show_width)\n",
    "        # 将灰度值缩放到0-char_len范围内，并取整\n",
    "        gray_value_index = int(gray[y][x] / 256 * char_len)\n",
    "        text += ascii_char[gray_value_index]\n",
    "    print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333333\n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                      00004    0000                                           \n",
      "                                                    70    01 00   00                                          \n",
      "                                                    03    0000     0                                          \n",
      "                                                   00     000     00                                          \n",
      "                                                  40    9900      00                                          \n",
      "                                                  90  999009  99900                                           \n",
      "                                                  00  99900  99900                                            \n",
      "                                                  50  99904 99990                                             \n",
      "                                     0070000       09 99909 99900 60006000                                    \n",
      "                                    0067777700     00 99900 999000677666610                                   \n",
      "                                   00676666676008  000 900000000777666666600                                  \n",
      "                                  8067666666666766700           0076666666600                                 \n",
      "                                  0676666666666666700000040200000776666666720                                 \n",
      "                                 507766666666666666676777700777666666666667603                                \n",
      "                               200666666666666666666666000  000277766666666660                                \n",
      "                             10076766666666666666760000         90000366776666000                             \n",
      "                            00   00000000000000000                        8      00                           \n",
      "                          10                                        000            0                          \n",
      "                         30                        0                                09                        \n",
      "                        20                                                           0                        \n",
      "                        01                      00  00             00   00           10                       \n",
      "                       20             02  800  90000000            0000000            09                      \n",
      "                       00           00  000  00 20 720              00000  99999999   08                      \n",
      "                        00          09   09   09                         990940902909 0                       \n",
      "                        00          0999 0099 05          000000000       9999999999 00                       \n",
      "                         00     000000998099 00740000       09900                    01                       \n",
      "                      9   55 4439   509 209  00      000     00                     00                        \n",
      "                        900005                          00    5                   00                          \n",
      "                       00 00     0000                    00  0000000000000      002                           \n",
      "                        000    03                00       00999960000301399000001                             \n",
      "                         30        000             0      0000             0000  00000                        \n",
      "                          0 909999  0       0 09   1      00                    0 000020                      \n",
      "                          30999990   90 08 9    999999    01              000000000  09 400                   \n",
      "                         00 009       90       94909099 902             0 0000 00  20 0    0                  \n",
      "                           0009                       0002             09 0 200000      9910                  \n",
      "                             00                       01000          00   00 8 0 90    3 10                   \n",
      "                           0002                       0            00  9   0      5 00009                     \n",
      "                          70                        8000       0000         00 348100                         \n",
      "                             973   000000080000000 00000 0000     9 8    0000                                 \n",
      "                                   0       90  04    0  00              008000                                \n",
      "                                      0  00 0000     0000                                                     \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义图像显示的高度和宽度\n",
    "show_height = 50\n",
    "show_width = 110\n",
    "\n",
    "# 定义一个ASCII字符列表\n",
    "ascii_char = list(\"0123456789 \")\n",
    "char_len = len(ascii_char)\n",
    "\n",
    "# 读取图像文件（请确保文件路径正确）\n",
    "pic = plt.imread(\".\\\\abc12.jpg\")  # 替换为你的图像文件路径\n",
    "\n",
    "# 获取图像的高度、宽度和通道数\n",
    "pic_height, pic_width, _ = pic.shape\n",
    "\n",
    "# 将RGB图像转换为灰度图像\n",
    "gray = 0.2126 * pic[:, :, 0] + 0.7152 * pic[:, :, 1] + 0.0722 * pic[:, :, 2]\n",
    "\n",
    "# 根据灰度值映射到相应的ASCII字符\n",
    "for i in range(show_height):\n",
    "    y = int(i * pic_height / show_height)\n",
    "    text = \"\"\n",
    "    for j in range(show_width):\n",
    "        x = int(j * pic_width / show_width)\n",
    "        # 将灰度值缩放到0-char_len范围内，并取整\n",
    "        gray_value_index = int(gray[y][x] / 256 * char_len)\n",
    "        text += ascii_char[gray_value_index]\n",
    "    print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                            00                                                                \n",
      "                                         00670770                                                             \n",
      "                                         77766666660   0   9                                                  \n",
      "                                       73766666667707740777067607609        00306609                          \n",
      "                                     00076666666666666666777777777777007007777776777                          \n",
      "                                  204 00000046776666776500000067666666666766666666406                         \n",
      "                              00                     200      000666666666666666676000                        \n",
      "                            00                     0  0          10007666766666666700                         \n",
      "                                             99999 0300       00  0   0000000000100000                        \n",
      "                                      0  0  9098098     0 0    000 999                200                     \n",
      "                         000        0 3  6  39  0  0 0    0       097090                                      \n",
      "                       00        0              00     0    0  0    0099                000                   \n",
      "                               70                                      7  00  0                               \n",
      "                      18       00            0000                               00       7                    \n",
      "                                            00   00                               09                          \n",
      "                       9           999999   0 40008            30   0                                         \n",
      "                        0000 901 90090909909                   0000000                    006                 \n",
      "                 000000  001000    999999989        0   7       00000             00                          \n",
      "              00 008180 000 0808                     00000400       904289999     00      00                  \n",
      "            400000     00007  00 00                   0000          0990909809                                \n",
      "            003007 7   8009009                        009             9999999    0        0                   \n",
      "          0000000     970000090                                                   80 030  0                   \n",
      "           099008000000060   00  0                                                  0080                      \n",
      "           00000000   00000    00                                                  00010                      \n",
      "                709000900093000 1059 9 000                                 00000300  030                      \n",
      "                0900 0990099   0   00706660         6                     0    05 009 00 000                  \n",
      "               00009 50009     0     06666661000000660          5000000000 00 00    4                         \n",
      "                               01  00   00000006666662                       0     07 90  0                   \n",
      "                               00  0899   0   0 0000000                     10         2                      \n",
      "                                00 00090      4      00                     00 9                              \n",
      "                                 200 003   200  9010907                    00 9                               \n",
      "                                   90   20000006 8000                    00                                   \n",
      "                                 70064000                              00   9                                 \n",
      "                                 00662620100000000007     9 710090000002600                                   \n",
      "                                 0062 390000                    05660    00                                   \n",
      "                                  900000005                      000 0 000                                    \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n",
      "                                                                                                              \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义图像显示的高度和宽度\n",
    "show_height = 50\n",
    "show_width = 110\n",
    "\n",
    "# 定义一个ASCII字符列表\n",
    "ascii_char = list(\"0123456789 \")\n",
    "char_len = len(ascii_char)\n",
    "\n",
    "# 读取图像文件（请确保文件路径正确）\n",
    "pic = plt.imread(\".\\\\abc13.jpg\")  # 替换为你的图像文件路径\n",
    "\n",
    "# 获取图像的高度、宽度和通道数\n",
    "pic_height, pic_width, _ = pic.shape\n",
    "\n",
    "# 将RGB图像转换为灰度图像\n",
    "gray = 0.2126 * pic[:, :, 0] + 0.7152 * pic[:, :, 1] + 0.0722 * pic[:, :, 2]\n",
    "\n",
    "# 根据灰度值映射到相应的ASCII字符\n",
    "for i in range(show_height):\n",
    "    y = int(i * pic_height / show_height)\n",
    "    text = \"\"\n",
    "    for j in range(show_width):\n",
    "        x = int(j * pic_width / show_width)\n",
    "        # 将灰度值缩放到0-char_len范围内，并取整\n",
    "        gray_value_index = int(gray[y][x] / 256 * char_len)\n",
    "        text += ascii_char[gray_value_index]\n",
    "    print(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5、 杨辉三角形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      *\n",
      "     ***\n",
      "    *****\n",
      "   *******\n",
      "  *********\n",
      " ***********\n",
      "*************\n",
      "  **\n",
      "  **\n"
     ]
    }
   ],
   "source": [
    "def print_tree(height):\n",
    "    # 绘制树冠部分\n",
    "    for i in range(height):\n",
    "        # 每一行先打印空格，使星号居中\n",
    "        print(' ' * (height - i - 1) + '*' * (2 * i + 1))\n",
    "    \n",
    "    # 绘制树干部分\n",
    "    trunk_width = height // 3\n",
    "    trunk_height = height // 3\n",
    "    trunk_space = (height - trunk_width) // 2\n",
    "    \n",
    "    for i in range(trunk_height):\n",
    "        print(' ' * trunk_space + '*' * trunk_width)\n",
    "\n",
    "# 调用函数并设置树的高度\n",
    "print_tree(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1    \n",
      "   1 1   \n",
      "  1 2 1  \n",
      " 1 3 3 1 \n",
      "1 4 6 4 1\n"
     ]
    }
   ],
   "source": [
    "def generate_pascals_triangle(num_rows):\n",
    "    \"\"\"\n",
    "    生成并打印杨辉三角形的前num_rows行\n",
    "    :param num_rows: 要生成的行数\n",
    "    \"\"\"\n",
    "    # 初始化一个空列表来存储杨辉三角形的每一行\n",
    "    triangle = []\n",
    "\n",
    "    # 逐行生成杨辉三角形\n",
    "    for i in range(num_rows):\n",
    "        # 初始化当前行的列表，第一个元素总是1\n",
    "        current_row = [1] * (i + 1)\n",
    "        \n",
    "        # 如果不是第一行，计算当前行中间的元素\n",
    "        for j in range(1, i):\n",
    "            current_row[j] = triangle[i - 1][j - 1] + triangle[i - 1][j]\n",
    "        \n",
    "        # 将当前行添加到三角形列表中\n",
    "        triangle.append(current_row)\n",
    "    \n",
    "    # 打印杨辉三角形\n",
    "    for row in triangle:\n",
    "        # 打印时，为了美观，可以在元素之间添加空格\n",
    "        print(' '.join(map(str, row)).center(num_rows * 2 - 1))\n",
    "\n",
    "# 示例：生成并打印前5行的杨辉三角形\n",
    "generate_pascals_triangle(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6、sin函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成x轴数据，从-2π到2π，每隔0.01取一个点\n",
    "x = np.arange(-2 * np.pi, 2 * np.pi, 0.01)\n",
    "# 计算y轴数据，即sin(x)\n",
    "y = np.sin(x)\n",
    "\n",
    "# 创建一个图形\n",
    "plt.figure()\n",
    "\n",
    "# 绘制sin(x)曲线\n",
    "plt.plot(x, y, label='sin(x)')\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('Plot of sin(x)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "\n",
    "# 添加网格\n",
    "plt.grid(True)\n",
    "\n",
    "# 显示图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
